AIMC Topic: Case-Control Studies

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Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system.

Development and Validation of a Deep-Learning Model to Predict Total Hip Replacement on Radiographs: The Total Hip Replacement Prediction (THREP) Model.

The Journal of bone and joint surgery. American volume
BACKGROUND: There are few methods for accurately assessing the risk of total hip arthroplasty (THA) in patients with osteoarthritis. A novel and reliable method that could play a substantial role in research and clinical routine should be investigate...

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.

BJOG : an international journal of obstetrics and gynaecology
OBJECTIVES: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these m...

Artificial intelligence for detecting keratoconus.

The Cochrane database of systematic reviews
BACKGROUND: Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on clinical examination and corneal imaging; though in the early stages, ...

In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study.

BMC medical informatics and decision making
BACKGROUND: Falls are one of the most common accidents in medical institutions, which can threaten the safety of inpatients and negatively affect their prognosis. Herein, we developed a machine learning (ML) model for fall prediction in patients with...

Correlation Between Statin Use and Symptomatic Venous Thromboembolism Incidence in Patients With Ankle Fracture: A Machine Learning Approach.

Foot & ankle specialist
BACKGROUND: Identifying factors that correlate with the incidence of venous thromboembolism (VTE) has the potential to improve VTE prevention and positively influence decision-making regarding prophylaxis. In this study, we aimed to investigate the c...

Serum myosin-binding protein c levels: a new marker for exclusion of preterm birth?

Turkish journal of medical sciences
BACKGROUND/AIM: To evaluate whether there is a relationship between serum myosin-binding protein C (MyBP-C) levels measured in the first trimester and the timing of delivery, and, if a relationship is detected, the potential of this relationship in d...

Detecting schizophrenia with 3D structural brain MRI using deep learning.

Scientific reports
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve clas...